SystemLink: Moving beyond Aquatic–Terrestrial Interactions to Incorporate Food Web Studies
Bibliographic record
Abstract
science projects, but also data providers, relatively early on.However, at the core and perhaps the most important factor contributing to the successful continuity of this project, is the dedication and engagement of the CLIC community scientists.We are incredibly fortunate that our community scientists are passionate and supportive of this work.To date, this project has collected over 52,500 lake ice phenology observations for 1008 lakes, and involved 935 monitors over the years (Fig. 3).As we work toward organizing these vast datasets, we look forward to exploring important questions on how climate change is affecting lake ice phenology across small and large lakes in the United States and identifying which lakes are most vulnerable to rapid ice loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".